It is well known that the reducing crop stress during the growing season is important to maximize crop quality, yield and economic return. As the crop grows and matures it is subject to a variety of factors that can negatively impact crop outcomes. The term crop stress as used in the present document to describe the crop stress resulting when the factors that cause crop stress during the crop life cycle and can be controlled or managed to some degree but are not managed as effectively as possible. Examples of these factors include nutrient and pH imbalance, insect and other pests, diseases, and other conditions which impact a crop during the growing season. The present document will use nutrient imbalance and in some cases a specific nutrient such as nitrogen as an example of a factor that can cause stress to describe the present invention. It should be noted that the methods and systems described herein apply also to pests and diseases even though the algorithm details may vary. As we describe the invention it should be noted that timeliness and ease of use are of particular importance. By timeliness we are addressing early detection of crop stress, determining the severity of the stress and responding to the stress quickly minimizing their negative impact. For the crop stress issues addressed in the present document corrective action needs to occur as quickly as possible to minimize the negative impact on the crop and it's quality and yield.
Matching the balance of nutrients available for a plant with the nutrient requirements of that plant, at any point in time during the plant life cycle, is critical to reduce stress and to maximize agricultural output and value. Matching available/required nutrient balance is especially important during key times in the plant's life cycle. Nutrient levels below what is required may result in yield loss or a decline in crop quality, and therefore economic loss. Nutrient levels above what is required can result in excessive costs and stressed plants, which is harmful for both plants and humans consuming them, and can negatively impact surface and ground water and has other harmful environmental effects.
While it is important to match the available nutrients to those required by each plant, this balance is difficult to achieve, especially in a scalable manner required in today's large-scale production agriculture operations where a farmer may have thousands of acres located in fields distributed over many miles. If a crop stress resulting from nutrient imbalance is identified shortly after it first begins to impact the plant and action is taken to add nutrients as soon as possible, permanent losses in yield, crop quality, and the resulting negative financial impact can be minimized.
The primary problem, which is addressed by the present invention, is the ability to determine the crop stress as quickly as possible, in-season, and ideally before any, or at least minimal, damage to the crop has occurred, and then make the farmer (grower, farm manager, consultant, supplier, contractor, or other person with the responsibility to monitor crop health, henceforth collectively identified as the user in the present document) aware of the stress such that corrective action can be taken immediately or soon thereafter. In addition, the present invention detects the stress and notifies the user in a scalable and cost effect manner. The process can be executed repeatedly and consistently and without requiring special agronomic or technical skills.
Continuing to use nutrients as an example, laboratory soil testing is currently widely used to determine the level of nutrients in the soil, however, it does not always accurately estimate the nutrients currently available for the crop planted in that soil. These tests are often performed many months before a crop is planted, in some cases years before, and may not reflect the nutrients available for a plant during a particular growing season. Another drawback of soil nutrient tests is that they do not take into account factors such as soil structure or biological activity, nor to they take into account weather and other factors, which affect the rate at which nutrients leave the soil into the surrounding environment. These, and other factors mean that the concentration of nutrients in the soil can rapidly deviate from the results of a soil test.
Plant tissue tests are almost always more accurate than soil tests when determining the condition of the plant and the need for additional nutrients. They are more accurate because they include additional information about the physiology of the crop and the actual status of crop nutrition rather than nutrients in the soil. Traditional tissue tests are destructive tests where a sample is sent to a laboratory for analysis. Non-destructive tissue tests have advantages over traditional destructive tests in that they can be performed in the field, and provide results much faster than laboratory tests, however these tests are subject to many of the same limitations and constraints as traditional laboratory tests.
The problems with laboratory tests (both soil and plant tissue) are that the results are often difficult to interpret. Also, there is a delay between when the samples are taken for analysis, and the delivery of that analysis to the farmer. This means the results may not be received by the farmer until the ideal time to take corrective action has passed. In addition, laboratory tests can be quite expensive, and they are usually performed randomly across a field and therefore can only, by the nature of sample testing, provide a loose estimate of the nutrient status over the entire field and may not reflect the variability found throughout the field and special problems in specific portions of the field.
Another method currently used to determine crop status is manual visual inspection, commonly called “scouting”. This is usually accomplished when the farmer or a contracted expert visually inspects the crop, by literally walking through or driving by the field. This technique is ineffective because the farmer can only inspect a small portion of the crop, is random in nature, and requires ambition, skill and interpretation in the person performing the scouting. This approach also becomes less practical as farm operation grows in scale.
Aerial visual inspection, using aircrafts, satellites, or other flying devices, is also used from time to time. This approach allows the farmer to scout a larger portion of the crop from an advantaged aerial position in a short time. This approach also depends on the skill of the farmer (or pilot, if the aircraft is manned) to make visual interpretations of the data gathered. Depending on the method of aerial inspection, this approach can be costly and therefore cannot be reasonably conducted on a regular, such as daily, basis. Satellites, another source of data, can typically capture imagery data infrequently, often every few weeks, and weather (e.g., clouds) can be an obstacle. The data is captured from a very high elevation making the resolution of the data problematic. Manned aircraft, satellite, or unmanned aerial vehicle (UAV) data is most often in a visual form, and actionable interpretation is difficult unless relying on a person skilled in the art. As a result, satellites and manned aircrafts cannot be depended on for timely crop status detection and are better used as a data source for long term planning.
Yet another approach to determine crop status is to use yield data produced by harvesting equipment, which is generated when the crop is harvested. Overlaying yield data, typically in the form of maps, from several years illustrates yield and yield variability over time, and can be useful when making long term plans. However, yield maps are not particularly useful during the growing season when it is important to detect crop status as timely as possible.
Finally new methods and devices, such as an attached device to a nutrient applicator or sprayer or other in-field device, may be used to detect a deficiency as the nutrient is being applied to the field or plant or an operation is performed in the field. When these machines travel back and forth across a field they dynamically make a determination of crop status and then apply the nutrients based on the analysis. These machines are costly, require a pass over the field, and are helpful when applying supplemental nutrients variably, but not as helpful when determining that there is a deficiency and when to take corrective action. Currently, the farmer is left with using one or a combination of all of these techniques, resulting in data for him or her to work with that is generally difficult to interpret, delayed in its usefulness, incomplete, costly, not scalable, and/or not science-based.
Different types and amounts of nutrient applications (such as manure or commercial nutrients) provide an additional set of factors to consider. Nutrient levels available to a plant can vary across the field depending on the chemical makeup of the nutrient, and how it reacts and is absorbed by the plants and soils. Plus, proper application is a frequent problem, caused by operator error, equipment malfunctions, and/or improperly calibrated application equipment.
The present inventor has recognized that current in-season crop status determination methods suffer from the same general problems, namely the lack of a repeatable, consistent, scalable, cost-effective, easy-to-interpret, and timely method to detect crop stress such as nutrient deficiencies in-season for today's production agriculture industry. Methods today are difficult to use and each come with their own set of technical, economic, and timing barriers. They do not take advantage of technologies such as timely, frequent, and cost-effective in-season data-gathering, mining, federation, and analysis to consistently and automatically make science-based determinations of crop status and crop stress such as nutrient status. Nor do current strategies adapt well to changes occurring in agriculture, namely, the economic need to maximize production, the increasingly larger agricultural operations, the increasingly more common use of unskilled workers, and the continual need to accommodate the occurrence of unplanned events such as inclement weather and climate change.
The present invention solves these problems by providing an analysis and alert system that can receive real time in-season crop data from UAVs (but not necessarily limited to that data source) dynamically combine the received data with additional data, process and analyze it to make determinations and notify the user, or other designated parties, of instances where there is a crop status that varies from parameters defined by the user. These parameters of measurement can be based on, for example, benchmarks established by the user him- or herself, benchmarks established by the present analysis and alert system, Internet-based research and other resources, and/or peer farmers and the results they are achieving. These notifications of crop status to the user or other designated parties can be provided on a timely basis such that corrective action can be taken consistently, repeatedly, and economically, and without agronomic or technical skill. These objectives are accomplished by employing technologies not previously exploited to such ends.